QC On Raw Data
Wenjun Liu
31 July, 2023
Last updated: 2023-07-31
Checks: 5 1
Knit directory: WenjunLiu_Thesis_Chapter4/
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library(ngsReports)
library(tidyverse)
library(yaml)
library(scales)
library(pander)
library(glue)
library(plotly)panderOptions("table.split.table", Inf)
panderOptions("big.mark", ",")
theme_set(theme_bw())config <- here::here("config/config.yml") %>%
read_yaml()
suffix <- paste0(config$tag, config$ext)
sp <- config$ref$species %>%
str_replace("(^[a-z])[a-z]*_([a-z]+)", "\\1\\2") %>%
str_to_title()samples <- config$samples %>%
here::here() %>%
read_tsv() %>%
mutate(
Filename = paste0(sample, suffix)
)config$analysis <- config$analysis %>%
lapply(intersect, y = colnames(samples)) %>%
.[vapply(., length, integer(1)) > 0]if (length(config$analysis)) {
samples <- samples %>%
unite(
col = group,
any_of(as.character(unlist(config$analysis))),
sep = "_", remove = FALSE
)
} else {
samples$group <- samples$Filename
}group_cols <- hcl.colors(
n = length(unique(samples$group)),
palette = "Zissou 1"
) %>%
setNames(unique(samples$group))Quality Assessment on Raw Data
rawFqc <- here::here("data/raw/FastQC") %>%
list.files(pattern = "zip", full.names = TRUE) %>%
FastqcDataList() %>%
.[fqName(.) %in% samples$Filename]plotSummary(rawFqc) +
theme(axis.text.y=element_blank()) 
Overall summary of FastQC reports
Library Sizes
A total of 792 libraries were contained in this dataset, with read totals ranging between 974,545 and 6,174,727 reads.
Across all libraries, reads were 40 bases.
plotReadTotals(rawFqc, pattern = suffix) +
theme(axis.text.y=element_blank())
Library Sizes for all supplied fastq files. Any samples run as multiple libraries are shown as the supplied multiple libraries and have not been merged.
Sequence Quality
plotBaseQuals(
rawFqc,
pattern = suffix,
dendrogram = TRUE,
cluster = TRUE
) +
theme(axis.text.y=element_blank())
Mean sequencing quality scores at each base position for each library
GC Content
plotGcContent(
x = rawFqc,
pattern = suffix,
species = sp,
gcType = "Trans",
usePlotly = TRUE,
dendrogram = TRUE,
cluster = TRUE
)GC content shown as the % above and below the theoretical GC content for the Hsapiens transcriptome.
ggplotly(
getModule(rawFqc, "Per_sequence_GC_content") %>%
group_by(Filename) %>%
mutate(
cumulative = cumsum(Count) / sum(Count)
) %>%
ungroup() %>%
left_join(samples) %>%
bind_rows(
getGC(gcTheoretical, sp, "Trans") %>%
mutate_at(sp, cumsum) %>%
rename_all(
str_replace_all,
pattern = sp, replacement = "cumulative",
) %>%
mutate(
Filename = "Theoretical GC",
group = Filename
)
) %>%
mutate(
group = as.factor(group),
group = relevel(group, ref = "Theoretical GC"),
cumulative = round(cumulative*100, 2)
) %>%
ggplot(aes(GC_Content, cumulative, group = Filename)) +
geom_line(aes(colour = group), size = 1/3) +
scale_x_continuous(label = ngsReports:::.addPercent) +
scale_y_continuous(label = ngsReports:::.addPercent) +
scale_colour_manual(
values = c("#000000", group_cols)
) +
labs(
x = "GC Content",
y = "Cumulative Total",
colour = "Group"
)
)GC content shown as a cumulative distribution for all libraries. Groups can be hidden by clicking on them in the legend.
Sequence Content
plotly::ggplotly(
getModule(rawFqc, module = "Per_base_sequence_content") %>%
mutate(Base = fct_inorder(Base)) %>%
group_by(Base) %>%
mutate(
across(c("A", "C", "G", "T"), function(x){x - mean(x)})
) %>%
pivot_longer(
cols = c("A", "C", "G", "T"),
names_to = "Nuc",
values_to = "resid"
) %>%
left_join(samples) %>%
ggplot(
aes(Base, resid, group = Filename, colour = group)
) +
geom_line() +
facet_wrap(~Nuc) +
scale_colour_manual(values = group_cols) +
labs(
x = "Read Position", y = "Residual", colour = "Group"
)
)